examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
<reponame>LexCC/spark-py/*
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 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
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package org.apache.spark.examples.mllib;

// $example on$
import scala.Tuple2;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.classification.NaiveBayes;
import org.apache.spark.mllib.classification.NaiveBayesModel;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.util.MLUtils;
// $example off$
import org.apache.spark.SparkConf;

public class JavaNaiveBayesExample {
  public static void main(String[] args) {
    SparkConf sparkConf = new SparkConf().setAppName("JavaNaiveBayesExample");
    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
    // $example on$
    String path = "data/mllib/sample_libsvm_data.txt";
    JavaRDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();
    JavaRDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{0.6, 0.4});
    JavaRDD<LabeledPoint> training = tmp[0]; // training set
    JavaRDD<LabeledPoint> test = tmp[1]; // test set
    NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
    JavaPairRDD<Double, Double> predictionAndLabel =
      test.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label()));
    double accuracy =
      predictionAndLabel.filter(pl -> pl._1().equals(pl._2())).count() / (double) test.count();

    // Save and load model
    model.save(jsc.sc(), "target/tmp/myNaiveBayesModel");
    NaiveBayesModel sameModel = NaiveBayesModel.load(jsc.sc(), "target/tmp/myNaiveBayesModel");
    // $example off$

    jsc.stop();
  }
}
